4.6 Article

A simple strategy to maximize water-reuse in multistage, multiproduct batch processes

期刊

CHEMICAL ENGINEERING RESEARCH & DESIGN
卷 168, 期 -, 页码 327-339

出版社

ELSEVIER
DOI: 10.1016/j.cherd.2021.02.012

关键词

Sustainability; Water reuse; Batch processes; Scheduling; Genetic algorithm; Textile processing

资金

  1. Indian Institute of Technology Madras
  2. Ministry of Human Resource Development, Government of India under the Scheme for Promotion of Academic and Research Collaboration (SPARC) [P418]

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Many countries are currently facing a severe water crisis. Small and medium enterprises in various industrial sectors struggle to deploy complex water-reuse strategies due to lack of instrumentation and automation. This paper proposes a simple and feasible water-reuse strategy that can result in significant water savings, as demonstrated in a textile industry case study.
Many countries are currently facing a severe water crisis. Further, in various industrial sectors, small and medium enterprises operate multistage, multiproduct batch processes that require large quantities of water. This work is motivated by the inability of such enterprises to deploy complex water-reuse strategies since their plants are neither well instrumented nor automated. The design and operational strategies of water reuse networks for multistage, multiproduct batch processes is inherently challenging due to the underlying discontinuities and other nonlinearities. In this paper, we propose an easy-to-implement water-reuse strategy that utilizes intermediate storage tanks with finite capacities. The strategy relies on a simple heuristic that maps discharge from specific stages to be stored in corresponding tanks. A fallback sequence is also provided in case the tank cannot accommodate the entire discharge volume. An analogous scheme for reusing water from the tanks in appropriate process stages is also proposed. In this paper, we demonstrate using a textile industry case study that this simple reuse strategy can result in significant water savings. For this, we develop an optimal production schedule using a genetic algorithm that simultaneously minimizes production makespan and the freshwater consumption. Our results show that for a set of 4 products with up to 14 production stages, 76% of the freshwater could be reduced if 13 tanks can be deployed. Even with 2 tanks, the proposed strategy results a 42% reduction of the freshwater requirements. Many countries are currently facing a severe water crisis. Further, in various industrial sectors, small and medium enterprises operate multistage, multiproduct batch processes that require large quantities of water. This work is motivated by the inability of such enterprises to deploy complex water-reuse strategies since their plants are neither well instrumented nor automated. The design and operational strategies of water reuse networks for multistage, multiproduct batch processes is inherently challenging due to the underlying discontinuities and other nonlinearities. In this paper, we propose an easy-to-implement water-reuse strategy that utilizes intermediate storage tanks with finite capacities. The strategy relies on a simple heuristic that maps discharge from specific stages to be stored in corresponding tanks. A fallback sequence is also provided in case the tank cannot accommodate the entire discharge volume. An analogous scheme for reusing water from the tanks in appropriate process stages is also proposed. In this paper, we demonstrate using a textile industry case study that this simple reuse strategy can result in significant water savings. For this, we develop an optimal production schedule using a genetic algorithm that simultaneously minimizes production makespan and the freshwater consumption. Our results show that for a set of 4 products with up to 14 production stages, 76% of the freshwater could be reduced if 13 tanks can be deployed. Even with 2 tanks, the proposed strategy results a 42% reduction of the freshwater requirements. ? 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

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